ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · GREENFIELD, MA · OCTOBER 2025
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/greenfield/october-2025-report
Monthly Traffic Safety Analysis
54 CRASHES IN
GREENFIELD, MA
OCTOBER 2025
In October 2025, Greenfield experienced 54 crashes, marking a 22.7% increase compared to the 44 crashes recorded in October 2024. Despite the rise in total crashes, the number of injuries saw a substantial decrease, falling from 25 in the prior period to 11 in the current period. This represents a 56% reduction in total injuries year-over-year, indicating a notable shift towards less severe crash outcomes.
54
▲ 22.7%was 44
Total Crash Events
0
Persons Killed
11
▼ -56.0%was 25
Persons Injured
5
▼ -28.6%was 7
Hit-and-Run Crashes
Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 1 crash with unreported severity is not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend indicates an increase in total crashes in Greenfield, with a rise from 44 crashes in October 2024 to 54 crashes in October 2025. This represents a 22.7% increase in the number of crash events year-over-year. Despite the increase in crash volume, total injuries decreased by 56%, from 25 to 11.
5
Hit-and-Run Crashes — October 2025
▼ -28.6% vs prior (7)
The number of hit-and-run crashes decreased from 7 in October 2024 to 5 in October 2025. Correspondingly, the hit-and-run rate declined from 15.9% of total crashes in the prior period to 9.3% in the current period. This indicates a downward trend in both the count and proportion of hit-and-run incidents.
Vulnerable Road User Casualties
0
Pedestrians Killed
0
Cyclists Killed
0
Motorists Killed
1
Pedestrians Injured
2
Cyclists Injured
8
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
The temporal patterns for crashes shifted year-over-year; the peak day for crashes moved from Friday in October 2024 with 10 incidents to Tuesday in October 2025, also with 10 incidents. The peak hour for crashes also changed, moving from 3 PM with 6 crashes in the prior period to 4 PM with 6 crashes in the current period. Crashes on Mondays increased from 3 to 10, while crashes on Fridays decreased from 10 to 8.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The distribution of crash severity changed significantly, with total injuries decreasing from 25 in October 2024 to 11 in October 2025. Serious injuries (Severity A) decreased from 3 to 1, minor injuries (Severity B) decreased from 13 to 6, and possible injuries (Severity C) decreased from 5 to 3. Consequently, crashes with no injury (Severity O) increased from 20 (45.5% share) to 43 (79.6% share), indicating a shift towards less severe outcomes despite the overall increase in crash count.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Most severe injury per crash record
Top Contributing Factors
The most frequent contributing factor in October 2025 was 'No improper driving' with 18 crashes, a substantial increase from 7 crashes in the prior period. 'Inattention' also increased, from 9 crashes in October 2024 to 12 crashes in October 2025. Conversely, 'Followed too closely' decreased from 5 crashes to 1 crash, and 'Disregarded traffic signs, signals, road markings' decreased from 4 crashes to 0 crashes in the current period.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
Crashes occurring under clear weather conditions remained constant at 38 for both periods, though they represented a smaller proportion of total crashes in the prior period. Crashes during cloudy conditions increased from 2 in October 2024 to 9 in October 2025. The number of crashes on wet road surfaces increased from 3 to 5 year-over-year, while crashes during daylight increased from 30 to 41.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Road surface condition field
Vehicles & Demographics
Toyota remained the top vehicle make involved in crashes, increasing from 12 vehicles in October 2024 to 21 in October 2025. Honda also saw an increase, from 7 vehicles to 13 vehicles year-over-year. Conversely, Chevrolet involvement decreased from 8 vehicles to 4 vehicles, and Nissan decreased from 7 vehicles to 5 vehicles. For persons, the 65+ age group decreased from 15 to 12, while the 16-20 age group increased from 10 to 13.
Top Vehicle Makes (96 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Vehicle unit records
17 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (98 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 25 mph zones decreased slightly from 22 in October 2024 to 20 in October 2025. Conversely, crashes in 30 mph zones increased from 10 to 13, and crashes in 5 mph zones increased from 2 to 4. Notably, there were 5 crashes in 65 mph zones in October 2025, where no crashes were recorded in this speed zone in the prior period.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2025-10-01 to 2025-10-31 · Posted speed limit at crash location
Data Sources & Methodology
Primary Data Source
All crash data in this report is sourced from Massachusetts Crash Data (MassDOT CDV), accessed programmatically via the Arcgis_yearly Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.
Data Retrieval
- Access method: Arcgis_yearly Open Data API (SoQL queries)
- Data format: Structured JSON via REST API
- Record types queried: Crash events, person records, and vehicle unit records
- Date filter applied: 2025-10-01 through 2025-10-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2025-10-01 through 2025-10-31 (31 days)
- Geographic scope: GREENFIELD, MA
- Total crash records analyzed: 54
- Total persons involved: 116
- Total vehicles involved: 96
Analytical Methodology
- Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
- Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
- Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
- Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
- Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
- Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
- AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.
Limitations & Disclaimers
- Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
- Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
- Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
- AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
- Percentages are calculated from reported data and are subject to rounding.
Non-Affiliation Disclosure
This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.
Data License
The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.
Corrections & Feedback
If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.
Suggested Citation
ThatCarHitMe.com (Injuria.ai). "GREENFIELD, MA Crash Intelligence Report: October 2025." Published June 21, 2026. Reporting period: 2025-10-01 to 2025-10-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/greenfield/october-2025-report
About the Publisher
ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.
Questions about this report's data or methodology: data@injuria.ai
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2025-10-01 – 2025-10-31
Generated: June 21, 2026 · All rights reserved